Watermarking Security

Bas, Patrick; Furon, Teddy; Cayre, François; Doerr, Gwenaël; Mathon, Benjamin

doi:10.1007/978-981-10-0506-0

Cited by 25 publications

(31 citation statements)

References 4 publications

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“…To take a closer look at how median filtering would affect the linear prediction coefficients, here we give a simple demonstration with k set to 1, and W set to 2 so that one coefficient is obtained for each direction. We estimated the linear prediction coefficients along 8 directions respectively, using 1,000 512 × 512 gray scale images from the BOWS2 database [1]. The results are reported in Fig.…”

Section: Linear Descriptormentioning

confidence: 99%

“…When the resolution of the probe image decreases, this phenomenon becomes more apparent due to the lack of samples. To give a quick demonstration in this section, we crop the 1,000 images from the BOWS2 database [1] into size 16 × 16. Simply considering 4 directions (i.e.…”

Section: Nonlinear Descriptormentioning

confidence: 99%

“…The histogram of LBP riu2 P ,R actually represents the distribution of order-statistics, which will reflect the influence of median filtering. As a result, the histogram of LBP riu2 P ,R is adopted as our nonlinear descriptor, denoted as N. Again, features of the 1,000 images from the BOWS2 database [1] are projected to 2D space by LDA in Fig. 3b Fig.…”

Section: Nonlinear Descriptormentioning

confidence: 99%

“…To evaluate the discrimination capability, we extracted L on the 1,000 images from the BOWS2 database [1] and then projected them to 2D space by means of LDA (see Fig. 3a).…”

Section: Linear Descriptormentioning

confidence: 99%

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Blind detection of median filtering using linear and nonlinear descriptors

Shen

Chen

2014

Multimed Tools Appl

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Recently, for the recovery of images' processing history, passive forensics of possible manipulations has attracted wide interest. In particular, due to highly non-linearity, median filtering (MF) usually serves as an effective tool of counter forensic techniques for other image operations. Therefore, the importance of median filtering detection is selfevident. In this paper, through analysing the pixel differences of images, we found the indications to study the complex correlations introduced by median filtering and adopt two sets of describing features to measure them. More Specifically, we utilize a linear prediction model for the differences of image that is computed along a specific direction and estimate the prediction coefficients to construct a linear descriptor L. Besides, we make use of the histogram of rotation invariant local binary pattern (LBP) to form a nonlinear descriptor N . According to our observation, we also propose an enhanced feature EF to further improve the detection performance. Based on these, we present a novel median filtering detection scheme incorporating both the linear and nonlinear descriptors. Extensive experiments are carried out, which demonstrate that our proposed scheme gains favorable performance comparing to state-of-the-art methods, especially for low resolution images and JPEG compressed images, and shows resistance to noise attack.

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Section: Linear Descriptormentioning

confidence: 99%

Section: Nonlinear Descriptormentioning

confidence: 99%

Section: Nonlinear Descriptormentioning

confidence: 99%

“…To evaluate the discrimination capability, we extracted L on the 1,000 images from the BOWS2 database [1] and then projected them to 2D space by means of LDA (see Fig. 3a).…”

Section: Linear Descriptormentioning

confidence: 99%

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Blind detection of median filtering using linear and nonlinear descriptors

Shen

Chen

2014

Multimed Tools Appl

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“…Fifteen SSLQ gray-scale images of size 512 512  are selected from image database BOWS-2 [11]. Their names are simple digital numbers including 1034, 1039, 1044, 1093, 1125, 266, 300, 397, 401, 538, 578, 696, 757, 783 and 843.…”

Section: Experiments and Analysismentioning

confidence: 99%

Reversible Data Hiding with Visual Effect Enhancement for Subjectively Low Quality Saturated Images

Chen¹,

Yuan²,

Hong³

et al. 2018

dtcse

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Abstract. Reversible data hiding with contrast enhancement (RDH-CE) hides data in cover images while improving the image contrast, and thus is particularly effective for un-saturated images and medical images. In RDH-CE, image contrast is enhanced by expanding and spreading the histogram towards the lowest and highest gray-scales. However, there exist quite a few low quality images which are saturated or near-saturated but with rather low visually-illustrated regions of interest. For these images, enhancing the visual effect in the texture areas obviously outweighs improving the image contrast and preserving image fidelity. This paper aims at improving the visual texture effect by adaptively selecting histogram bins and reducing the distortion to structure similarity. Experimental results show that the proposed method obviously enhances the visual effects in texture areas while reducing structure distortion.

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